Intelligent remote sensing data analyis by MTA SZTAKI

In
this work we have proposed a Multi-frame Marked Point Process
model for automatic target detection and tracking in Inverse Synthetic
Aperture Radar (ISAR) image sequences. For purposes of dealing
with high ISAR noise, we obtain the optimal target sequence by
an energy minimization process, which simultaneously considers
the observed image data and prior geometric interaction constraints
between the target appearances in the consecutive frames. Finally,
a robust permanent scatterer detetection step is introduced to
support the target identification process. Evaluation is performed
on real ISAR image sequences of ship targets.

This work is conducted in cooperation with Marco Martorella from the Radar Laboratory of the University of Pisa, under the APIS Project (Array Passive ISAR adaptive processing, 2010-2012) funded by the Europan Defense Agency Project. For more information please contact Csaba Benedek

Motivation

Automatic
detection, tracking and characterization of ship scattering centers
in airborne Inverse Synthetic Aperture Radar (ISAR) image sequences
are key tasks of Automatic Target Recognition (ATR) systems that
make use of ISAR data. ISAR images are often used for classifying
and recognizing targets, since they can provide useful two-dimensional
features, where other imaging techniques, such as SAR processing,
fail. A number of ATR techniques based on sequences of ISAR images
have been proposed in the literature. Some of them directly utilize
the 2D ISAR images, whereas others attempt a 3D signal reconstruction
before dealing with the classification problem.

Recently, Marked Point Processes (MPP) have become popular in
object recognition tasks, since they can efficiently model the
noisy spectral appearance and the geometry of a target using a
joint configuration energy function. However, conventional MPP
models deal with the extraction of static objects in single images
or a pair of remotely sensed photos. Conversely, in the addressed
scenario, a moving target must be followed across several frames.
Thus, we construct a novel Multiframe MPP framework which simultaneously
considers data-object consistency in the individual ISAR images
and interactions between objects in the consecutive frames.

Figure: Ship target representation in an ISAR image: (a) input image with a single ship object

Besides
the target scatterer's extraction, another issue is to detect
characteristic features in the ship objects which provide relevant
information for the identification process. For this purpose,
we identify permanent bright points in the imaged targets, which
are produced by stronger scatterer responses from the illuminated
ship. Due to the presence of speckle, image defocus and scatterer
scintillation, a significant number of missing and false scatters
appear in the individual frames. Permanent scatters are identified
by applying a kernel density estimation for the empirical distance
histograms.